Automated biofilm region recognition and morphology quantification from confocal laser scanning microscopy imaging

Abstract

Staphylococcus aureus is an opportunistic human pathogen and a primary cause of nosocomial infections. Its biofilm forming capability is an adaptation strategy utilized by many species of bacteria to overcome stressful environmental conditions and provides both resistance to antimicrobial treatments and protection from the host immune system. This paper addresses a growing demand for an objective, fully automated method of biofilm structure description with standardized parameters that are independent of user input. In this study, we used watershed segmentation to analyze and compare confocal laser scanning microscopy (CLSM) images of two S. aureus strains with different biofilm-forming capabilities. Results are compared with manual calculations as well as the commonly used COMSTAT software.

Critical review on biofilm methods

“…Several of the newer packages offer automatic thresholding which can reduce the workload of the experimenter and remove the operator induced bias, but give different results with different implementations (Lewandowski & Beyenal, 2014). However, unless special conditions apply, most advanced automatic thresholding mechanisms give comparable and acceptable results (Lewandowski & Beyenal, 2014;Zielinski et al, 2011).…”

“…Ideally, the determination of what parts of an image is biomass or not should be individual per stack slice and within a slice. A technique called segmentation allows for such higher fidelity of determining the extent of a biofilm (Zielinski et al, 2011). However, since this is technically complicated most programs use different, simpler approaches for determining the threshold.…”